![]() ![]() Hence, it is not feasible to isolate and examine their separate effects on diseases. First, each type of food contains multiple nutrients with complex interactions and latent cumulative relationships. There are several reasons for this shift. The changes from the first Dietary Guidelines for Americans in 1980 to those in 2015 show that the focus of nutritional epidemiology has gradually shifted from single nutrients to dietary patterns, focusing on features of the entire diet. As an evolving subject, there is always scope for deriving dietary patterns through new analytic methodologies.ĭietary intake, one of the essential factors that influence health, varies widely among individuals. Selection of the most appropriate method mainly depends on the research questions. However, future research is needed to evaluate these emerging methods’ performance in terms of reproducibility, validity, and ability to predict different outcomes. While all statistical methods for dietary pattern analysis have unique features and serve distinct purposes, emerging methods warrant more attention. This paper presents a landscape review of the existing statistical methods used to derive dietary patterns, especially the finite mixture model, treelet transform, data mining, least absolute shrinkage and selection operator and compositional data analysis, in terms of their underlying concepts, advantages and disadvantages, and available software and packages for implementation. There are some emerging methods that have rarely or never been reviewed or discussed adequately. While many statistical methods exist, the literature predominantly focuses on classical methods such as dietary quality scores, principal component analysis, factor analysis, clustering analysis, and reduced rank regression. Dietary pattern analysis is a promising approach to understanding the complex relationship between diet and health.
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